ISAR Imaging Research Articles

Estimating the Hotspot Area Observed by Earth Imaging Satellite via ISAR Image Sequence

Estimating the Hotspot Area Observed by Earth Imaging Satellite via ISAR Image Sequence

Three-Dimensional High-Resolution Space-Borne ISAR Imaging With Compact Antenna Configuration for Large Rotational Angle

Three-Dimensional High-Resolution Space-Borne ISAR Imaging With Compact Antenna Configuration for Large Rotational Angle

Few-shot Classification for ISAR Images of Space Targets by Complexvalued Patch Graph Transformer

Few-shot Classification for ISAR Images of Space Targets by Complexvalued Patch Graph Transformer

Deep Unfolding Network for ISAR Imaging Based on Hypernetwork

Deep Unfolding Network for ISAR Imaging Based on Hypernetwork

Slow-Spinning Spacecraft Cross-Range Scaling and Attitude Estimation Based on Sequential ISAR Images

Slow-Spinning Spacecraft Cross-Range Scaling and Attitude Estimation Based on Sequential ISAR Images

Integrated Convolution Network for ISAR Imaging and Target Recognition

Integrated Convolution Network for ISAR Imaging and Target Recognition

A novel spaceborne ISAR imaging approach for space target with high-order translational motion compensation and spatial variant MTRC correction

ABSTRACT For spaceborne ISAR imaging of space targets, due to the high-speed relative motion between the radar and targets, both the translational and rotational motion exhibit the high-order characteristics. Conventional translational motion compensation (TMC) methods are ineffective in handling such high-order translational motion. Moreover, the migration through resolution cell (MTRC) stems from the presence of the high-order spatial variant phase of the echo signal, resulting in severe defocusing of ISAR images. In this paper, a novel spaceborne ISAR imaging approach for space targets is proposed, which incorporates the high-order TMC and the high-order MTRC correction. First, the slant range model for spaceborne ISAR imaging is derived as a third-order polynomial form. Based on this model, a modified high-order TMC method is proposed, which utilizes the minimum entropy method combined with the Broyden – Fletcher – Goldfarb – Shanno (BFGS) algorithm. The proposed method leverages orbital model information to obtain the initial TMC coefficients, enabling TMC to be completed more accurately and effectively. Then, a modified polynomial keystone transform (PKT) is proposed to correct the high-order MTRC. The PKT-based method effectively addresses both the high-order and first-order MTRC corrections simultaneously. Finally, a spaceborne ISAR imaging approach is proposed to obtain well-focused ISAR images. Simulated and real data results validate the analysis of the spaceborne ISAR imaging model and demonstrate the effectiveness of the spaceborne imaging approach for space targets proposed in this paper.

Photonics-enabled joint communication-radar system with improved detection performance and looser symbol length limitation based on resampling and phase compensation

A photonics-enabled joint communication-radar (JCR) system by employing a multiple phase-shift keying (MPSK)-sliced linearly frequency-modulated (LFM) signal for simultaneous range measurement, microwave imaging, and data transmission is proposed and experimentally demonstrated with improved detection performance and looser symbol length limitation. A two-stage modulation in conjunction with optical filtering is utilized at the radar transmitter for the generation of the frequency-and-bandwidth-multiplied MPSK-sliced LFM signal while resampling and phase compensation operations are introduced at the radar receiving end to break the detection performance limitation induced by the communication symbol length when MPSK-sliced LFM signal is used. We demonstrate the concept experimentally. BPSK-sliced and QPSK-sliced LFM signals centered at 12 GHz are generated for JCR systems. When the time difference between the radar echo and the de-chirping reference is close to or greater than one symbol length, range measurement and microwave imaging that are hard to be fulfilled due to the random phase jumps of the de-chirped signal can be successfully realized via resampling and phase compensation. Experiments show that the proposed resampling and phase compensation method is valid for improving radar detection performance and loosening the communication symbol length limitation. Data communication with a data rate of up to 400 Mbps and ISAR imaging with a radar signal bandwidth of 1 GHz are successfully realized.

High-Resolution ISAR Imaging Method for Maneuvering Targets Based on a Hybrid Transformer

High-Resolution ISAR Imaging Method for Maneuvering Targets Based on a Hybrid Transformer

Novel approach for sparse aperture inverse synthetic aperture radar imaging via improved alternating direction method of multipliers algorithm

AbstractAs a new algorithm to achieve sparse aperture inverse synthetic aperture radar (SA‐ISAR) imaging, the alternating direction method of multipliers (ADMM) suffers from selection of model parameters and the inability to discriminate the target and non‐target components. Therefore, an improved ADMM (I‐ADMM) algorithm is proposed to solve these problems. First, the particle swarm optimisation (PSO) algorithm is utilised to obtain the optimal model parameters. Aiming to adequately preserve the target's shape information in the ISAR image, a modified image entropy (IE) is proposed to be the objective function of the PSO. Then, a new regularisation function is obtained from the ADMM based ISAR image. The proposed algorithm can enhance the target components and suppress the non‐target components by utilising the regularisation function. Finally, results based on the simulated and measured data show that the proposed I‐ADMM algorithm not only achieves at least 9.4% improvement in IE but also greatly improves the image quality as visually compared with the traditional ADMM algorithm which validates the effectiveness and superiority of the proposed algorithm. [Correction added on 13/09/23, after first online publication: In the following sentence in abstract “Finally, results based on… the proposed algorithm.” 10% was changed as 9.4%.]

Two-dimensional gridless super-resolution method for ISAR imaging

Two-dimensional gridless super-resolution method for ISAR imaging

Tapered Resistive Sheets and FSS Absorber Loading for the Wideband RCS Reduction of the Elliptic Surface at TM Polarization

A wide-band radar cross section (RCS) reduction of the elliptic surface at TM polarization is reported in this communication. The main scattering sources and the corresponding scattering mechanisms of the elliptic surface are accurately identified based on the ISAR imaging technique and the high-frequency approximation theory. The frequency selective surface (FSS) absorber is employed to suppress the specular reflection, and the tapered resistive sheets (TRSs) are used to handle the scattering from the trailing edges. Samples have been fabricated and measured in the anechoic room, both the simulation and measurement results illustrate that 10-dB RCS reduction is achieved within 1.35-4.9 GHz at TM polarization.

Design of Wideband FMCW Radar Transceiver System Using Photonic Elements.

This paper proposes a FMCW radar transceiver with photonic elements. The proposed radar system is efficiently designed by budget analysis, and a wideband signal is generated using photonic elements. To verify the performance of the proposed radar system, field tests including changes in bandwidth are conducted. The results confirm that the resolution of ISAR images improves as the bandwidth increases as expected through the budget analysis.

An ISAR and Visible Image Fusion Algorithm Based on Adaptive Guided Multi-Layer Side Window Box Filter Decomposition

Traditional image fusion techniques generally use symmetrical methods to extract features from different sources of images. However, these conventional approaches do not resolve the information domain discrepancy from multiple sources, resulting in the incompleteness of fusion. To solve the problem, we propose an asymmetric decomposition method. Firstly, an information abundance discrimination method is used to sort images into detailed and coarse categories. Then, different decomposition methods are proposed to extract features at different scales. Next, different fusion strategies are adopted for different scale features, including sum fusion, variance-based transformation, integrated fusion, and energy-based fusion. Finally, the fusion result is obtained through summation, retaining vital features from both images. Eight fusion metrics and two datasets containing registered visible, ISAR, and infrared images were adopted to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed asymmetric decomposition method could preserve more details than the symmetric one, and performed better in both objective and subjective evaluations compared with the fifteen state-of-the-art fusion methods. These findings can inspire researchers to consider a new asymmetric fusion framework that can adapt to the differences in information richness of the images, and promote the development of fusion technology.

ISAR Imaging of Non-Stationary Moving Target Based on Parameter Estimation and Sparse Decomposition

This paper studies the inverse synthetic aperture radar imaging problem for a non-stationary moving target and proposes a non-search imaging method based on parameter estimation and sparse decomposition. The echoes received by radar can be thought of as consisting of chirp signals with varying chirp rates and center frequencies. Lv’s distribution (LVD) is introduced to accurately estimate these parameters. Considering their inherent sparsity, the signals are reconstructed via sparse representation using a redundant chirp dictionary. An efficient algorithm is developed to tackle the optimization problem for sparse decompositions. Then, by using the reconstructed data, adaptive joint time–frequency imaging techniques are employed to create high-quality images of the non-stationary moving target. Finally, the simulated experiments and measured data processing results confirm the proposed method’s validity.

Three-Dimensional Geometry Reconstruction Method from Multi-View ISAR Images Utilizing Deep Learning

The three-dimensional (3D) geometry reconstruction method utilizing ISAR image sequence energy accumulation (ISEA) shows great performance on triaxial stabilized space targets but fails when there is unknown motion from the target itself. The orthogonal factorization method (OFM) can solve this problem well under certain assumptions. However, due to the sparsity and anisotropy of ISAR images, the extraction and association of feature points become very complicated, resulting in the reconstructed geometry usually being a relatively sparse point cloud. Therefore, combining the advantages of the above methods, an extended factorization framework (EFF) is proposed. First, the instance segmentation method based on deep learning is used for the extraction and association of a number of key points on multi-view ISAR images. Then, the projection vectors between the 3-D geometry of the space target and the multi-view ISAR images are obtained, using the improved factorization method. Finally, the 3D geometry reconstruction problem is transformed into an unconstrained optimization problem and solved via the quantum-behaved particle swarm optimization (QPSO) method. The proposed framework uses discretely observed multi-view range–Doppler ISAR images as an input, which can make full use of the long-term data of space targets from multiple perspectives and which is non-sensitive to movement. Therefore, the proposed framework shows high feasibility in practical applications. Experiments on simulated and measured data show the effectiveness and robustness of the proposed framework.

Parameters estimation method and ISAR imaging of multi-target with complex maneuvering

Parameters estimation method and ISAR imaging of multi-target with complex maneuvering

GNSS-Based Passive Inverse SAR Imaging

The utilization of global navigation satellite system (GNSS) signals for remote sensing has been a hot topic recently. In this article, the feasibility of the GNSS-based passive inverse synthetic aperture radar (P-ISAR) is analyzed. GNSS-based P-ISAR can generate the two-dimensional image of a moving target, providing an estimation of the target size, which is very important information in target recognition. An effective GNSS-based P-ISAR moving target imaging algorithm is proposed. First, a precise direct path interference (DPI) suppression method is derived to eliminate the DPI power in the detection channel. Then, the P-ISAR signal processing method is established. Due to the large synthetic aperture time, the Doppler profile of the ISAR image will defocus if directly performing the Fourier transform. As a solution, a parametric autofocusing and cross-range scaling algorithm is specially tailored for the GNSS-based P-ISAR. The proposed algorithm cannot only focus and scale the ISAR image, but also provide an estimation of the cross-range velocity of the target. Simulation with an airplane target is designed to test the signal processing method. Finally, an experiment is conducted with a civil airplane as the target and GPS satellites as the illumination source. Focused ISAR image is successfully acquired. The estimated length and velocity of the target are approximately consistent with ground truth, which are obtained by the flight record. The potential of the GNSS-based P-ISAR on multistatic operations is also illustrated by the fusion of the ISAR images obtained using different satellites as illumination sources.

Three-Dimensional Polarimetric InISAR Imaging of Non-Cooperative Targets

A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3D imaging method for non-cooperative targets is proposed in this paper. 3D imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2D ISAR imaging. In the previously reported single-polarimetry InISAR based 3D imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3D ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3D imaging of the target is possible. To validate this proposed Pol-InISAR based 3D imaging approach, both simulated and real datasets are taken under consideration.

CVAE: An Efficient and Flexible Approach for Sparse Aperture ISAR Imaging

The use of traditional compressed sensing methods in sparse aperture inverse synthetic aperture radar (SA-ISAR) imaging suffers from high computational burdens and complicated formula derivations. To solve these problems, a complex-valued autoencoder (CVAE) architecture trained to efficiently obtain ISAR images from incomplete echoes is proposed in this letter. In addition, a training set is generated for network training via self-supervised learning. Instead of using multiple simulated scenes as a training set, multiple range cells in a simulated scene are used based on two considerations. First, reducing the complexity of the dataset allows for faster and easier training of the network. Second, it is flexible as it does not require retraining when handling scenes with various numbers of range cells. In this way, due to the properties of matrix operations, similar time complexity can be achieved in both single measurement vector (SMV) and multiple measurement vector (MMV) cases. Notably, the network can be trained using only the training set and tested on the simulated electromagnetic (EM) data and the data obtained from an operational radar system. Our experimental results validate the superiority of the proposed method in terms of better performance, better robustness to noise, and higher computational efficiency.